AndreaBozzo/ceres-open-data-index huggingface dataset:
ExSSPIR Reference database in order to improve standardization & interoperability between spectrometer using deep learning

[Lien vers la fiche source] Near infrared spectrometry allows the measurement of the absorbance or reflectance of the infrared region of the electromagnetic spectrum of a sample whose composition one wishes to determine in part. If the principle is common to the various applications, the spectrum produced depends on the specific conditions of measurement and more particularly on the sample (e‧g. granulometry, moisture content), the environment (e‧g. temperature), the spectrometer (e‧g. model, brand, age) but also on the interaction between these 3 types of variables. If it is possible to minimize the influence of the product and the environment (i‧e. by standardizing the sample preparation and measurement conditions), the spectrum remains the product of the sample/spectrometer pair. This dependence of the calibration on the spectrometer on which the measurements have been performed limits the generality of the results and the possibilities of data exchange in the network. It imposes a fastidious work of standardization associated with a loss of precision as soon as the spectrometer park evolves, that the latter gets old or that the predictive model is used on another spectrometer, even from the same manufacturer. In order to increase the generality of the results, it is necessary to compare the evolution of the spectra of the same products on several spectrometers. The specific objective was to create and fill in a standard multi-machine and multi-product database in near infrared spectroscopy. Four repetition of 66 samples belonging to five sample types (e‧g. yam and cassava flour) were acquired on 8 different spectrometers (TANGO, Lab Spec 5000, LabSpec4, MicroNIR1, MicroNIR2, Quality Spec, NIRSystem5000, Camera FX17). A database of 2640 spectra was built. In the next periods, this database will allow to study the interoperability between measurement devices of the network, especially by using artificial intelligence (e‧g. generative adversial network, variational autoencoder).
NIRS Database on Fresh Cassava, Wet Mashed Fufu & Dried Fufu Flour for Dry matter, Starch & Amylose Calibrations at NRCRI, Nigeria

[Lien vers la fiche source] The dataset contains spectra data of samples of fresh cassava roots and fufu mash from the Uniform Yield and NCRP Trials harvested in Umudike and Otobi in Nigeria obtained using QualitySpec Trek: S-10016 used to predict starch, dry matter and amylose.
Consolidated database on trees in control plots in the M'Baïki forests (CAR) between 1982 and 2022

[Lien vers la fiche source] Contains data collected on three control plots: * plot 13 (plot 3 of Boukoko 1) * plot 16 (plot 6 of Boukoko 2) * plot 24 (plot 4 of La Lolé) The main following variables were recorded: * tree number, species name, tree coordinates on the plots, adjusted diameters, original diameters, observations, status and corresponding measurement dates. This is a dataset repository for the manuscript: Bénédet, F., Gourlet-Fleury, S., Allah-Barem, F., Baya, F., Beina, D., Cornu, G., Dimanche, L., Dubiez, É., Forni, É., Freycon, V., Mortier, F., Ouédraogo, D.Y., Picard, N., Rossi, V., Semboli, O., Yalibanda, Y., Yongo-Bombo, O. & Fayolle, A. 40 years of forest dynamics and tree demography in an intact tropical forest at M’Baïki in central Africa. Sci Data 11, 734 (2024). doi: [10.1038/s41597-024-03577-6]
Ionomic data of a 20x20 m plot of shrubland in Plaine des Lacs (New Caledonia)

[Lien vers la fiche source] High species richness is observed in certain shrublands on infertile substrates. Mineral nutrients are likely to be the primary limiting resources in these ecosystems, and below-ground plant interactions may be crucial to understanding their diversity. Using ionomics, we investigated whether there were nutritional variations between plant species that coexist in a shrubland located in an edaphically extreme environment. We set up a 20 m × 20 m plot in a diverse shrubland (“maquis”) on ultramafic (infertile) substrate, in which we sampled all 475 plants taller than 1 m and characterized their ionome (22 elements). In our study, 37 species were identified in the plot, representing all major forms of mycorrhizal symbioses, as well as nitrogen-fixing plants, cluster rooted and parasitic plants. Notably, both nickel hyperaccumulating and manganese hyperaccumulating species were present. Hypervolume approaches were used to assess ionome overlap among the nine most abundant species, with the results revealing limited overlap. Moreover, it was observed that the rarest species in the plot also had the most functionally distinct features.
Degraded land reclamation actions in 4 countries along the route of the Great Green Wall

[Lien vers la fiche source] Land degradation constitutes a significant challenge in the countries along the Great Green Wall. Substantial efforts have been directed towards implementing Sustainable Land Management (SLM) practices aimed at combating land degradation. In this context, the dataset "Degraded land reclamation actions in 4 countries along the Great Green Wall" is a contribution to the effort (i) to collect, centralize and describe degraded land reclamation actions (DLRAs) and (ii) to identify the different stakeholders in the countries along the Great Green Wall. It concerns 4 countries: Niger, Burkina Faso, Ethiopia and Senegal. Seven main steps for the data collection process regarding DLRAs were necessary for the creation of a spatially referenced data table that ensures traceability of DLRAs and enables them to be monitored. Beyond geolocation, three other monitoring criteria were considered during the data integration process: the description of DLRA, details about implementation, details about stakeholders involved. The dataset corresponds to what was made available by the countries using the same collection method. It has to be said that this dataset has yet to be completed for each country. It highlights disparities between countries in terms of data completeness. Despite the supposed lack of data and the disparities between countries, the work has made it possible to define and describe a data collection method that can be applied in all other GMV countries. It define the information that is essential for the traceability and monitoring of a land recovery action. Finally, it has made it possible to compile an initial substantial dataset on actions to reclaim degraded land, the players involved and the areas concerned. This dataset, which needs to be expanded, forms a solid basis for further data collection in the 4 countries, and possibly in other countries along the GMV route.
Weed diversity and floral potential in olive groves from a French Mediterranean region

[Lien vers la fiche source] The data set describes the weed species diversity and abundance, floral quantity and traits in 16 olive groves along a pedoclimatic gradient in the French Mediterranean region (Occitanie) at seven dates from September 2021 to June 2022. Floral traits related to pollinator attraction and rewards were measured at the species level in a greenhouse experiment in 2022 (n=22). The data set includes a detailed description of pedoclimatic conditions and agricultural practices of all the surveyed fields.
Farmer economic and social surveys in the Chrey Bak catchment, Cambodia: Rainy Season Rice Farming Activities, and Perceptions and Usage of Termite Mounds

[Lien vers la fiche source] This dataset comprises raw data collected from a field survey conducted at Stung Chrey Bak catchment in Kampong Chhnang Province, Cambodia, between March and April 2024. A total of 589 farmers from 54 villages of Tuek Phos and Rolea B'ier districts were interviewed to gather information on rainy season rice farming activities from 2021 to 2023 and their perceptions and uses of termite mounds. The survey questions are organized into two main sections. The first section contains 39 common questions for all farmers, addressing topics such as household identification, general farming activities, rice plot details (size, location, and ownership), climate risk perceptions (droughts and floods), and soil fertility perceptions. It includes detailed records of rainy season rice farming inputs and outputs, such as rice varieties, seed usage, and production, as well as labor, machinery, fertilizer, pesticide, and fuel usage. The second section is specific to two groups: farmers without termite mounds in their rice plots (Case 1) and farmers with at least one termite mound in their rice plots (Case 2). In Case 1, questions aim to understand farmers' past experiences with termite mounds, the reasons for their absence in fields, and their general perceptions of these formations. In Case 2, the questions assess farmers' experiences in utilizing the ecosystem services provided by termite mounds, their motivations to maintain them, the challenges related to their conservation, and their overall perceptions of termite mounds. The dataset is categorized in an Excel file across eleven sheets, with each sheet corresponding to specific survey elements: • Sheet 1: Contains household identification, general farming activities, household member information, rice plot details, and perceptions of climate risks and soil fertility (questions 1 to 18). • Sheets 2 and 3: Focus on rainy season rice farming activities (questions 19 and 20), detailing rice varieties, seed inputs, and total rice yield for 2021 to 2023. • Sheets 4 to 9: Address various farming inputs: labor (questions 21 to 26), machinery (questions 27 to 30), organic fertilizers (questions 31 to 32), chemical fertilizers (questions 33 to 34), pesticides (questions 35 to 38), and fuel (question 39). • Sheet 10: Contains information for Case 1, focusing on farmers without termite mounds in their rice plots (questions 40 to 53). • Sheet 11: Contains information for Case 2, focusing on farmers with at least one termite mound in their rice plots (questions 40 to 58).
Spatial representations of texts from the BVLAC and PADI-WEB corpora

[Lien vers la fiche source] [EN] This dataset contains the result of the extraction of spatial information from textual data in in the form of STR, a graphical representation composed of spatial entities connected by their spatial relationships. Each graph node is associated with an identifier from Geodict ([available here]). The textual data use to generate these representations come from two corpora: * PADI-WEB Corpus: [Available here] * Corpus BV-LAC : [Available here] [FR] Ce jeu de données contient le résultat de l'extraction d'informations spatiales dans des données textuelles sous la forme de STR, une représentation graphe composée d'entités spatiales connectés par leur relations spatiales. Chaque nœud des graphes est associé à un identifiant provenant de Geodict ([disponible ici]). Ces données textuelles proviennent de deux corpus : * Corpus PADI-WEB : [Disponible ici] * Corpus BV-LAC : [Disponible ici]
Cover plant and weed control experimental data from field trials in Reunion (2016-2018)

[Lien vers la fiche source] The dataset included experimental data from 10 trials with cover crops performed in Reunion from 2016 to 2018. The files in the dataset included: * "Trial and species description" : description of the trials and cover plant species * "DATARAW" files: raw measurements of plant coverage, height, number of leaves, phenology and biomass performed in the different trials. * "DATA_calculated: provides trait variables calculated from the raw measurements per species and trials.
Code and data for: Not all species will migrate poleward as the climate warms: the case of the seven baobab species in Madagascar

[Lien vers la fiche source] This repository includes code and data for the following article: TAGLIARI Mario M., Pascal DANTHU, Jean-Michel LEONG POCK TSY, Cyrille CORNU, Jonathan LENOIR, Vítor CARVALHO-ROCHA, Ghislain VIEILLEDENT. 2021. Not all species will migrate poleward as the climate warms: the case of the seven baobab species in Madagascar. Global Change Biology. doi: [10.1111/gcb.15859]. The data/baobabs/ folder includes the occurrence dataset for the seven baobab species present in Madagascar (file data_Adansonia‧csv). This file is the result of years of field inventories in Madagascar by botanists and ecologists working at [CIRAD]. A development version of this code is available on GitHub: [https://github.com/ghislainv/baobabs_mada].
Geodict: an integrated gazetteer

[Lien vers la fiche source] [EN] Geodict is a gazetteer where 12 millions spatial entities are referenced. Each entry is associated with basic yet detailed information such as multi-lingual labels, polygon of boundaries, coordinates, class, etc. Geodict data are extracted from famous dataset: Geonames, Wikidata and OpenStreetMap. [FR] Geodict est un gazetier avec 12 millions d'entités spatiales référencées. Chaque entrée est associée avec un ensemble de données basiques mais précise comme les labels multilingues, le(s) polygone(s) des frontières, les coordonnées, etc. Les données renseignées dans Geodict sont extraites à partir de jeux de données populaires : Geonames, Wikidata, OpenStreetMap. Latest updates: * Add aliases e‧g., Paris <=> Ville Lumière, etc. * Add Inclusion Chain (use Geonames and Wikidata), e‧g., Paris \in Ile de France \in France, and so forth. Geodict is available [here] as an Elasticsearch database.
Plateforme et langage de modélisation Ocelet

[Lien vers la fiche source] Ocelet est un langage métier dédié à la modélisation de dynamiques spatiales. Ocelet est doté d'un environnement de développement de modèles et de simulation : Ocelet Modelling Platform. [http://www.ocelet.fr/] (2018). Ocelet est notamment basé sur le concept de graphe d'interactions pour manipuler l'information géographique. Ce concept facilite la formalisation de connaissances sur les relations entre entités (spatiales ou non) et sur ce qui provoque l'évolution du système à travers les interactions portées par ces relations. Ce concept permet aussi de représenter des relations spatiales indépendamment de la forme des entités géographiques manipulées (couches thématiques vecteur, ou raster). Le langage Ocelet et la plateforme de modélisation sont développés au sein de l'Unité Mixte de Recherche TETIS (Territoire Environnement Télédétection et Information Spatiale), en particulier par des chercheurs du Cirad. La version 1 d'Ocelet nommée Carmine (2014 Degenne P.; Lo Seen D.), offrait toutes les fonctionnalités de modélisation et simulation à partir d'informations géographiques organisées en couches thématiques vecteurs. La version 2 d'Ocelet nommée Sulfur (2018 Castet, M.; Degenne, P.; Lo Seen, D.), a apporté la possibilité d'intégrer des données de type raster dans les modèles, avec par exemple des interactions vecteur-raster, et raster-raster. Il est donc possible d'intégrer directement dans un modèle des données de télédétection, y compris des séries temporelles d'images.
Water Level Data from the Prek System of the Cambodian Mekong Delta (2020-2024)

[Lien vers la fiche source] As in many tropical deltas globally, annual floods shape the livelihoods of the largely rural population in the Cambodian Mekong delta. Agricultural cycles are keyed to the flood arrival, peak, and recession, and fish populations depend on inundated floodplains for their regeneration. However, as factors like climate change and hydropower infrastructure development are altering the Mekong's hydrology, the inundation dynamics of its deltaic floodplains are shifting as well. Several studies have assessed the general changes of river discharge and flood extent on a basin- or delta-wide scale. Yet the sustainable development of this region is relying on dynamics at more local and specific scales, which have not been addressed so far. This dataset includes water level data from nine differential pressure loggers installed in the Kandal region in Cambodia. One of them was installed directly in the Bassac river, the others in Prek channels - canals that diverge perpendicularly from the river mainstream to establish a connection with low-lying floodplains and acting as vectors for both flooding and drainage. Data for each logger is in a separate folder, which contains the data, a Readme file with metadata, a diagram of the installation, a map of its location, and a photograph of the logger in situ.
Climate data rescue 1: Annales du bureau central météorologique (ABCM)

[Lien vers la fiche source] ### Content of the ABCM The Annales du Bureau Central Météorologique first appeared in 1880 under the direction of Éleuthère Mascart. The ABCM succeeded the Bulletin météorologique de l'Association scientifique de France, published from 1871 until 1876 by M. Bellegrand. The latter focused mainly on the publication of rainfall data for river basins, with the main aim of forecasting floods. The ABCM gradually extended the scope of the data published to include temperature, pressure, humidity, etc. The first issue in 1882 published data for 1878, then for 1877. The last volume, published in 1926, published data for 1920. From 1882 to 1885, the Annales were divided into 4 volumes (32 volumes in all) 1. Studies of thunderstorms in France ; 2. Bulletin des observations françaises and revue climatologique 3. Rainfall in France 4. General meteorology From 1886 to 1914, the Annales were divided into 3 volumes (87 volumes in total) 1. Memoirs 2. Observations 3. Rainfall in France The volumes 2 of observations were themselves divided into three sections: * A. French observations (pages numbered A3 to A...); * B. Algerian observations (numbered B3 to B...); * C. Observations done in French colonies and abroad (numbered C3 to C...) It was in this last section C that the first meteorological data from Indo-China were published from 1890 onwards. From 1915 to 1920, only 4 volumes were published (2 volumes for the years 1915-1917 and 2 volumes for the years 1918-1920). These 4 volumes contained no data for Indochina. In 1924, data for the years 1915, 1916 and 1917 were published. The introduction to this volume states that for a long time it had been difficult for the Central Office to compile the 1915 data. It also states that observations made in the colonies were no longer published in the ABCM from that date onwards. For observations made in Algeria, Tunisia and Morocco, it is specified that these data are published by the meteorological services of these countries, for the other colonies and in particular for Indochina, the introduction to this volume does not specify the periodical in which they can be found. ### Contents of the dataset This dataset contains all the Indochinese station data published between 1890 and 1914 in part 3 of volume 2 of the Annales (of which we have made an exhaustive inventory). ### Meteorological data coverage 1890-1914
Climate data rescue 2: Bulletin économique de l'Indochine (BEI)

[Lien vers la fiche source] ### Contents of the BEI The Bulletin économique de l'Indochine is a monthly review reporting on the economic development of Indochina in all sectors of activity, and published from July 1898 to 1945 (283 volumes). The Bulletin's dual propagandist and utilitarian aims (to help European settlers in their various endeavors) meant that it published a large number of in-depth studies on the development of mines, of transport network, of trade, of European and indigenous agriculture, plantations, industrial plants, irrigation system, etc. Many scientific and technical expertise of crops, medicinal plants, forest production, the quality of timber... are also published. With the same aim, BEI publishes numerous statistics and observation data, and specially daily meteorological data from the main meteorological stations of Indochina. It also contains numerous articles on the different climates of Indochina (see collection D in this database). ### Contents of the dataset This dataset contains all the daily meteorological data that have been published in the BEI. From July 1898 to June 1904 : daily temperature and rain for 8 stations in the first year and up to 40 stations at the end of this period (See in the tree structure tab file named: 1. Standard tables of daily data). From June 1904 to december 1905, as the number of stations increased rapidly, publishing all the data became complicated from an editorial point of view, especially for a journal not dedicated to meteorology. This explains why, from issue no. 32 in August 1904 (data for June 1904), the tables changed form, still providing daily data for rainfall, but for temperatures only maximum and minimum of the month with the days of these last data (See in the tree structure tab file named: 2. New tables). From January 1906 to December 1916, no meteorological data are published in the BEI From January 1917 to December 1922, for September 1923, and for the 12 months of 1930 , the BEI published the monthly diagram from the central observatory of Phu Lien, showing daily observations of temperature, rainfall, atmospheric pressure, cloud cover, winds, etc. (See in the tree structure tab file named: 3. Diagramm of Phu Liên). From April 1932 to August 1937, the BEI started to publish again meteorological observations but only monthly data of temperature, evaporation and rainfall (See in the tree structure tab file named: 4. Monthly data of the 1930's). After 1937, no more meteorological data was published in the BEI. See also in the tree structure tab, file 5 Special tables and graphs from different periods. ### Meteorological data coverage 1898-1905 ; 1917-1922 ; Sep 1923 ; Jan 1930-June 1931 ; Apr 1932-Aug1937
Climate data rescue 3: Bulletin Mensuel des Observations (BMO)

[Lien vers la fiche source] ### Content of the BMO The Bulletin Mensuel des Observations have been published for the first time in 1910 under the direction of Georges Le Cadet, director of the Service Météorologique de l'Indochine, and later on by the following directors (Etienne Bruzon, B. Guilmet, A. Romer) until 1942. The collection recovered at Trappes comprises 119 issues (fascicles), each corresponding to one month: * 1910: Jan, Feb, Mar (3 fascicles) * 1925: Complete year (12 fascicles) * 1926: Jan-Oct (10 fascicles) * 1927: Complete year (12 fascicles) * 1928: Complete year (12 fascicles) * 1929: Complete year (12 fascicles) * 1930: Complete year (12 fascicles) * 1937: Complete year (12 fascicles) * 1938: Complete year (12 fascicles) * 1939: Jan-Jun (6 fascicles) * 1940: Complete year (12 fascicles) * 1941: Complete year (12 fascicles + 1 fascicle of supplement) * 1942: Jul (1 fascicle) As can be seen, the number of lacks is very high. There are particularly two big gaps: 15 years between 1910 and 1925 and 6 years between 1930 and 1937. In the current state of our research, it is still impossible to say whether these gaps are due to an incomplete collection of the Météo France in Trappes or to the fact that the BMO was not regularly published. The second hypothesis seems more likely, as the BMO collection held at the BNF has fewer issues than that of Météo France but has approximately the same gaps; and as the first page of the December 1930 issue bears the following information: "Publication of the BMO is suspended until further notice". This terse form is not followed by any explanation. Nevertheless, we would ask users of this database to let us know if they can provide any additional issues missing in the present dataset. ### Content of the dataset The present dataset content all the data of the collection of BMO recovered in Météo France Trappes. ### Meteorological data coverage 1910; 1925-1930; 1937-1942